1. Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
- Author
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Audrey Duval, Jérôme Fernandes, David R M Smith, Didier Guillemot, Koen B. Pouwels, Laura Temime, Lulla Opatowski, Bich-Tram Huynh, Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Clinique de soins de suite et réadaptation [Choisy-Le-Roi], Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), The work was supported directly by internal resources from the French National Institute for Health and Medical Research, the Institut Pasteur, the Conservatoire National des Arts et Métiers, and the University of Versailles–Saint-Quentin-en-Yvelines/University of Paris-Saclay. This study received funding from the French Government’s 'Investissement d’Avenir' programme, Laboratoire d’Excellence 'Integrative Biology of Emerging Infectious Diseases' (Grant ANR-10-LABX-62- IBEID). DS is supported by a Canadian Institutes of Health Research Doctoral Foreign Study Award (Funding Reference Number 164263) as well as the French government through its National Research Agency project SPHINX-17-CE36-0008-01. KP is supported by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (grant number NIHR200915)., AP-HP/Universities/Inserm COVID-19 research collaboration, ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-17-CE36-0008,SPHINx,Diffusion de pathogènes au sein des réseaux de soins : une étude de modélisation(2017), Bich-Tram, Huynh, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Diffusion de pathogènes au sein des réseaux de soins : une étude de modélisation - - SPHINx2017 - ANR-17-CE36-0008 - AAPG2017 - VALID, Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], and Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM)
- Subjects
Male ,0301 basic medicine ,MESH: Coronavirus Infections ,Testing ,Pooling ,lcsh:Medicine ,[MATH] Mathematics [math] ,law.invention ,MESH: Long-Term Care ,0302 clinical medicine ,MESH: Practice Guidelines as Topic ,Public health surveillance ,law ,Mass Screening ,MESH: COVID-19 ,Public Health Surveillance ,030212 general & internal medicine ,[MATH]Mathematics [math] ,0303 health sciences ,Public health ,Mathematical modelling ,MESH: Public Health Surveillance ,Transmission (medicine) ,General Medicine ,3. Good health ,[STAT]Statistics [stat] ,Transmission (mechanics) ,Practice Guidelines as Topic ,Female ,Epidemiological Monitoring ,medicine.symptom ,Coronavirus Infections ,Research Article ,medicine.medical_specialty ,Infectious disease surveillance ,Pneumonia, Viral ,030106 microbiology ,Asymptomatic ,Contact network ,Long-term care ,03 medical and health sciences ,Group tests ,medicine ,Humans ,MESH: SARS-CoV-2 ,MESH: Mass Screening ,Mass screening ,030304 developmental biology ,MESH: Humans ,SARS-CoV-2 ,business.industry ,lcsh:R ,COVID-19 ,Outbreak ,Group testing ,MESH: Male ,[STAT] Statistics [stat] ,Transmission dynamics ,Computational modelling ,MESH: Pneumonia, Viral ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,Emergency medicine ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,business ,MESH: Female - Abstract
This work resulted from the MOD-COV Project (Modelling of the hOspital Dissemination of SARS-CoV-2), a collaboration between the Institut Pasteur, the Conservatoire Nationale des Arts et Métiers, and the AVIESAN/REACTing working group “Modelling SARS-CoV-2 dissemination in healthcare settings”, whose members we thank (Niccolo Buetti, Christian Brun-Buisson, Sylvie Burban, Simon Cauchemez, Guillaume Chelius, Anthony Cousien, Pascal Crepey, Vittoria Colizza, Christel Daniel, Aurélien Dinh, Pierre Frange, Eric Fleury, Antoine Fraboulet, Marie-Paule Gustin, Lidia Kardas-Sloma, Elsa Kermorvant, Jean Christophe Lucet, Chiara Poletto, Rodolphe Thiebaut, Sylvie van der Werf, Philippe Vanhems, Linda Wittkop, Jean-Ralph Zahar).We also thank the i-Bird Study Group (Anne Sophie Alvarez, Audrey Baraffe, Mariano Beiró, Inga Bertucci, Pierre-Yves Boëlle, Camille Cyncynatus, Florence Dannet, Marie Laure Delaby, Pierre Denys, Matthieu Domenech de Cellès, Eric Fleury, Antoine Fraboulet, Jean-Louis Gaillard, Boris Labrador, Jennifer Lasley, Christine Lawrence, Judith Legrand, Odile Le Minor, Caroline Ligier, Lucie Martinet, Karine Mignon, Catherine Sacleux, Jérôme Salomon, Thomas Obadia, Marie Perard, Laure Petit, Laeticia Remy, Anne Thiebaut, Damien Thomas, Philippe Tronchet, Isabelle Villain), as well as Jacob Barrett for helpful discussion.; International audience; Background: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources.Methods: We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing.Results: In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections.Conclusions: COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.
- Published
- 2020